Some digital image processing applications designed to enhance the appearance of the processed digital images take explicit advantage of the noise characteristics associated with the source digital images. For example, Keyes et al. in U.S. Pat. No. 6,118,906 describe a method of sharpening digital images which includes the steps of measuring the noise components in the digital image with a noise estimation system to generate noise estimates; and sharpening the digital image with an image sharpening system which uses the noise estimates. Similarly, digital imaging applications have incorporated automatic noise estimation methods for the purpose of reducing the noise in the processed digital images as in the method described by Anderson et al. in U.S. Pat. No. 5,809,178.
In commonly-assigned U.S. Pat. No. 5,923,775, Snyder et al. disclose a method of image processing which includes a step of estimating the noise characteristics of a digital image and using the estimates of the noise characteristics in conjunction with a noise removal system to reduce the amount of noise in the digital image. The method described by Snyder et al. is designed to work well for individual digital images and includes a multiple step process for the noise characteristics estimation procedure. First the residual signal is formed from the digital image obtained by applying a spatial filter to the digital image. This first residual is analyzed to form a mask signal which determines what regions of the digital image more and less likely to contain image structure content. The last step includes forming a second residual signal and sampling the residual in image regions unlikely to contain image structure content to form the noise characteristic estimation.
In U.S. Pat. No. 6,069,982, Reuman et al. describe a method of automatically estimating the noise characteristics of a digital image acquisition device which includes providing predetermined default values for the spatial noise characteristics of the digital image acquisition device, gathering information related to the spatial noise characteristics of the digital image acquisition device; generating replacement data in response to said gathered information; and updating said predetermined default spatial noise characteristics associated with the digital image acquisition device with said replacement data. In particular the method disclosed by Reuman et al. estimate the standard deviation (derived from the variance) as a function of the grey-level (pixel value) and the spatial frequency characteristics of the noise. The noise characteristics, such as a table of standard deviation values as a function of grey-level, are provided as the default values. Each digital image to be processed is analyzed which includes the calculation of statistical quantities in the gathering of information step. These statistical quantities and the default values are combined to calculate the updated replacement noise characteristic values.
The method described by Reuman et al. further teaches a method of selecting between a predetermined table of statistics and a using a captured digital image of interest to estimating noise characteristics. If there is no predetermined table of statistics, only then Reuman et al. use the captured digital image to estimate noise characteristics.